Tool health monitoring and classifications with virtual metrology and incoming wafer monitoring enhancements

Abstract

A method of evaluating tool health of a plasma tool is provided. The method includes providing a virtual metrology (VM) model that predicts a wafer characteristic based on parameters measured by module sensors and in-situ sensors of the plasma tool. A classification model is provided that identifies a plurality of failure modes of the plasma tool. An initial test is performed on an incoming wafer to determine whether the incoming wafer meets a preset requirement. The wafer characteristic is predicted using the VM model when the incoming wafer meets the preset requirement. A current failure mode is identified using the classification model when the wafer characteristic predicted by using the VM model is outside a pre-determined range.

What is claimed is: 
     
       1. A method of evaluating tool health of a plasma tool, the method comprising:
 providing a classification model that identifies a plurality of failure modes of the plasma tool; 
 performing an initial test on an incoming wafer by performing a measurement on the incoming wafer; 
 determining that the incoming wafer meets a preset requirement based on the initial test, in response to determining that the incoming wafer meets a preset requirement based on the initial test, executing a plasma etching process, and predicting a wafer characteristic associated with the plasma etching process using a virtual metrology (VM) model that is configured to predict the wafer characteristic based on parameters measured by module sensors and in-situ sensors of the plasma tool; 
 determining that the wafer characteristic predicted by using the VM model is outside a pre-determined range and identifying a current failure mode using the classification model; and 
 based on the current failure mode, adjusting a recipe of the plasma etching process or taking a corrective action for the plasma tool. 
 
     
     
       2. The method of  claim 1 , wherein providing the classification model comprises:
 determining predictor parameters; 
 removing collinearity among the predictor parameters to obtain key predictor parameters; 
 selecting a subset of the key predictor parameters based on relevance to the plurality of failure modes; and 
 building the classification model using the subset of the key predictor parameters. 
 
     
     
       3. The method of  claim 2 , wherein determining the predictor parameters comprises:
 determining target wafer characteristics; and 
 determining failure modes for the target wafer characteristics based on occurrence and sensitivity of the failure modes so that the parameters from the module sensors and the in-situ sensors are classified into different categories for the failure modes. 
 
     
     
       4. The method of  claim 3 , wherein a fault detection model is, constructed with one or more parameters from the module sensors without using parameters from the in-situ sensors, the method further comprising adding the one or more parameters from the module sensors to a first subgroup of predictor parameters. 
     
     
       5. The method of  claim 4 , wherein building a fault detection model entails using one or more parameters from the in-situ sensors, the method further comprising adding the one or more parameters from the in-situ sensors to a second subgroup of predictor parameters. 
     
     
       6. The method of  claim 5 , wherein determining the predictor parameters further comprises:
 obtaining a third subgroup of predictor parameters by processing the parameters from the module sensors and the in-situ sensors using domain knowledge including knowledge of the plasma tool, a plasma process associated with the plasma tool, metrology and/or the wafer; and 
 processing the third subgroup of predictor parameters to remove error and variance. 
 
     
     
       7. The method of  claim 5 , further comprising building a VM model associated with a failure mode using the second subgroup of predictor parameters. 
     
     
       8. The method of  claim 5 , wherein the classification model comprises a plurality of fault detection models. 
     
     
       9. The method of  claim 5 , further comprising integrating a plurality of fault detection models into a single multi-class classification model by applying a machine learning algorithm. 
     
     
       10. The method of  claim 2 , wherein providing the classification model using the subset of the key predictor parameters comprises regression analysis that includes at least one of a logistic regression, a support vector machine regression, a decision tree regression or a linear regression. 
     
     
       11. The method of  claim 1 , wherein performing the initial test comprises:
 measuring a reflectivity of the incoming wafer; 
 providing a test model that predicts the wafer characteristic based on the reflectivity; and 
 predicting the wafer characteristic using the test model. 
 
     
     
       12. The method of  claim 11 , wherein the incoming wafer meets the preset requirement when the wafer characteristic predicted by using the test model is within a preset range. 
     
     
       13. The method of  claim 1 , wherein the current failure mode allows for a process control, and the recipe of the plasma etching process is adjusted. 
     
     
       14. The method of  claim 13 , wherein the current failure mode comprises a worn part of the plasma tool. 
     
     
       15. The method of  claim 1 , wherein the current failure mode does not allow for a process control, and the corrective action is taken. 
     
     
       16. The method of  claim 15 , wherein:
 the failure mode includes deposition on a chamber wall, and 
 the corrective action includes seasoning to reset the chamber. 
 
     
     
       17. The method of  claim 15 , wherein:
 the failure mode includes radio frequency (RF) generator power output, and 
 the corrective action includes RF generator service. 
 
     
     
       18. The method of  claim 1 , wherein the module sensors include at least one of a pressure manometer, a gas flow meter or RF power meter. 
     
     
       19. The method of  claim 1 , wherein the in-situ sensors include at least one of a reflectometer, a plasma sensor, an RF sensor or a voltage and current (VI) sensor. 
     
     
       20. The method of  claim 1 , further comprising determining that the wafer characteristic predicted by using the VM model is within the pre-determined range, and continuing to process a new wafer. 
     
     
       21. The method of  claim 1 , wherein the wafer characteristic is selected from the group consisting of a critical dimension (CD), an etch rate (ER), particles and defects.